1 / 47

Frank A. Voelker, DVM, DACVP Flagship Biosciences

Pathology Visions: Approaches to Tissue-based Image Analysis in Pharmaceutical Research and Development. Frank A. Voelker, DVM, DACVP Flagship Biosciences. Topics……. Introduction Applications and Challenges Concepts and Approaches Analytical Strategies Guidelines and Pitfalls

xaria
Download Presentation

Frank A. Voelker, DVM, DACVP Flagship Biosciences

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Pathology Visions: Approaches to Tissue-based Image Analysis in Pharmaceutical Research and Development Frank A. Voelker, DVM, DACVP Flagship Biosciences

  2. Topics……. • Introduction • Applications and Challenges • Concepts and Approaches • Analytical Strategies • Guidelines and Pitfalls • Various Examples Using Genie™ • Summary

  3. Pharmaceutical Research and Development Oncology Clinical Trial • Samples usually morphologically variable • Intragroup collection conditions variable • Target tissue limited to neoplasm, stroma Early Drug Discovery • Samples usually morphologically similar • Intragroup collection conditions adjusted similar • Target tissue variable depending on project • Samples usually morphologically similar • Intragroup collection conditions adjusted similar • Target tissue variable depending on project Preclinical Safety Testing Different approaches for each setting are required because of different group sizes, tissue types and tissue heterogeneity. However, segregation of target from nontarget tissue during analysis is a major challenge in all settings

  4. Available Analytical Tools……. Pixel Count IHC Deconvolution Co-localization IHC Nuclear Rare Event Membrane Angiogenesis

  5. pS6 Ser235 Immunostain of Breast Carcinoma Introducing the Concept of “Target Tissue” Analysis Analysis of average cytoplasmic stain intensity using the pixel count tool may be useful in evaluating a neoplasm if there is little background or nonspecific staining.

  6. Analysis of Target Tissue In it’s Simplest Terms….. • Count and measure simple structures/objects. • Measure area of defined regions/stain. • Measure intensities of stain as a percentage of defined regions. • Combinations of 1, 2 and 3 above.

  7. Two Different Approaches for Analysis Quantify Histomorphologic Change • Cellular Hypertrophy/Atrophy • Cell Numbers • Tissue Infiltrates (eg. Fibrosis) • Other Structural Alterations Usually measuring area or number Quantify Substances using Special Stains Usually measuring area and/or intensity • Histochemistry • IHC • ISH

  8. Morphologic Approach…… Quantifying Common Microscopic ToxPath Changes using H&E or Special Stains • Liver: Hepatocellular hypertrophy, bile duct hyperplasia, necrosis, acute and chronic inflammation, extramedullary hematopoiesis, periportal fibrosis, fatty change, glycogen accumulation. • Kidney: Tubular basophilia, hyaline droplet degeneration, casts, tubular necrosis. • Spleen: Lymphoid hyperplasia/atrophy, extramedullary hematopoiesis • Lung: Alveolar edema, pneumonia, congestion. • Heart: myocardial fibrosis. • Adrenal gland: cortical hypertrophy, cortical vacuolation. • Skin: Acute and chronic inflammation, acanthosis Biggest Problem: Distinguishing target from nontarget tissue

  9. Methods for Defining the Target Tissue for Analysis • Define the target tissues for analysis by existing algorithms using common (eg H&E) or special (eg IHC) staining procedures. • Define the target tissues for analysis using Genie™ • Assist in defining target tissues in 1 and 2 above by using the positive and negative pen tools. A high degree of accuracy in target tissue definition will assure a high degree of accuracy in the final analysis.

  10. Mouse Liver - Hepatocellular Hypertrophy Total Hepatocyte Nuclei = 199 Average Nuclear Size =140 µm² 706 nuclei/mm² Total Hepatocyte Nuclei = 167 Average Nuclear Size = 160 µm² 508 nuclei/mm² Algorithm: IHC Nuclear (cell-based)

  11. Cyclin D1 Immunostain of Human Breast Carcinoma Use of the IHC Nuclear Analysis Tool to Determine Percent and Degree of Positivity of Neoplastic Cell Nuclei. Stromal Nuclei are Excluded from Evaluation.

  12. Quantifying Inflammation in Tissue using the Nuclear Analysis Tool… Different cell types often can be distinguished from each other in the same tissue based on nuclear diameter. Here lymphocyte nuclei are smaller than mammary carcinoma nuclei. This makes it possible to count lymphocyte numbers per unit area of tissue cross section to determine degree of infiltration. Algorithm: IHC Nuclear (cell-based)

  13. Sirius Red Stain Depicting Myocardial Fibrosis in a Mouse Analysis Tool: Color Deconvolution (area-based) Precision in level of section is required for accurately comparing amounts of fibrosis between treatment groups

  14. Fibrosis in Livers of Zucker Rats T T Fenofibrate Rat No. 5 Control Rat No. 12 T T C Pioglitazone Rat No. 3 Compound X Rat No 2 C X F P Variations in fibrosis (blue) about small portal triad veins (T) as depicted using Masson’s Trichrome stain

  15. Quantitation of PAS Stain for Glycogen in Livers of DIO Mice Administered XXX Using the Aperio Image Analysis System Analysis Tool: Color Deconvolution (area-based) PAS-stained Section Aperio Markup Image

  16. Three Possible Strategies for Measuring Immunohistochemistry Stains using the Positive Pixel Count Analysis Tool • Quantitate the percentage area of all brown pixels in the section or in selected areas of the section. • If the chromagen staining is very extensive in the target cell population, measure only the brownest (darker) pixels in selected areas of the section. • If the chromagen staining is uniform in character and very extensive in the target cell population, measure stain intensity as an index of concentration.

  17. Percent of Liver Tissue Staining for Transferrin Receptor(CD71) in Female Mice by Immunohistochemistry Measuring all of the brown pixels in the sample area ** % * Control 100 mg/kg 250 mg/kg 1000 mg/kg * p  .01 **p  .001

  18. Cytochrome p450 Reductase Immunostaining of Centrilobular Hepatocytes Widespread staining with centrilobular distribution of more intense staining

  19. Quantitation of Cytochrome p450 Reductase Immunostaining of Centrilobular Hepatocytes by Aperio Original Image Markup Image Measuring only the area of more intense stain Color deconvolution (area-based)

  20. Quantitation of VEGF Immunostaining in Livers of Mice administered XXX for 52 Weeks Control Females Control Males 1000 mg/kg Males 1000 mg/kg Females Comparing stain intensity

  21. Genie™…….. Introducing the concept of using histology pattern recognition software as a preprocessing machine to segregate target from nontarget tissue during analysis Strategies

  22. Tumor Cell-Specific Biomarker Analysis using Genie Histology Pattern Recognition Software Genie mark-up image. Tumor cells = blue Pulmonary adenocarcinoma stained for pS6-Ser240 IHC nuclear analysis of tumor cells Positive pixel count analysis of tumor cells

  23. Analysis of Study Sample Groups by Genie™ Morphologically Variable Samples Trained Individually for Genie Target Tissue Selection Targeted Tissue Selection and Isolation by Genie™ Subsequent Uniform Analysis of Isolated Target Tissue for area/intensity Separate target tissue training of each sample does not affect final target tissue analysis.

  24. Immunostain Analysis of Human Breast Tumor Tissue Micro Arrays Multiple Genie™ Training Classifiers may be needed in analysis of a TMA slide because of tumor heterogeneity.

  25. Tumor Cell-Specific Biomarker Analysis of TMA Breast Tumor Samples using Genie Histology Pattern Recognition Software IHC Genie Mark-up Positive Pixel Mark-up

  26. Manual Inclusion/Exclusion as a Method of Defining the Target Tissue Use of the Positive and Negative Pen Tools…… • Usually done when it is difficult to define target tissue components in the image by stain specificity and/or by pattern recognition software. • May be laborious and time-consuming to perform manual inclusion or exclusion especially with complex tissue patterns. • Potentially introduces subjective evaluation by the operator which may further increase error. • However, may be the easiest and most rapid method of helping to define the target tissue.

  27. Use of Positive and Negative Pen Tools Similar IHC staining of fibronectin and secretion droplets in this xenograft tumor with subsequent poor differentiation by the Genie™ classifier required the use of the negative pen tool to assist in quantitating fibronectin using the IHC deconvolution algorithm.

  28. “H Score”: A Convention for the Simultaneous Assessment of Both Area and Intensity of Stain pS6 Ser235 Immunostain of Squamous Cell Carcinoma in Human Lung Estimation of Average stain intensity should take into account negative-staining regions of target tissue as well as positive-staining regions

  29. Scoring is a Convention for Determining Average Stain Intensity of Target Tissue • With the old subjective scoring method, the pathologist visually scored staining features of cells (eg. cytoplasmic, nuclear, or membranous staining) by intensity of stain according to grades 0, 1+ , 2+ or 3+ using the following formula: • (1)x(%1+)x(%Area) + (2)x(%2+) x (%Area) + (3)x(%3+)x(%Area) = “H” Score (For a maximum of 300) Now “H” Score evaluation is automatically calculated in Aperio’s IHC Deconvolution Algorithm using attribute outputs in the following similar formula: (Nwp/Ntotal)x(100) + Np/Ntotal)x(200) + Nsp/Ntotal)x(300) = “H” Score Where: Nwp = Number of weakly positive pixels Np = Number of moderately positive pixels Nsp = Number of strongly positive pixels Ntotal = Total number negative + positive pixels Not available with IHC Nuclear and Membrane Algorithms

  30. Some Guidelines for Analysis of Slides from Experimental Studies • Take care to assure immediate optimal fixation for all tissue samples. Uniformity of handling as well as fixation time is important. • Staining procedures for all slides in a study need to be performed simultaneously in a single batch to assure uniformity of stain. • Sampling must be strictly representational as well as consistent. Care must be taken to assure exact uniformity of analysis with respect to anatomical location (eg. Tissue trimming, sectioning) • A preliminary evaluation of image analysis tools between some slides of varying stain intensities will help assure that analysis values are established optimally for all slides in the study.

  31. Consistency of Study Conditions can Affect Morphometric Analysis Variations in duration of fasting prior to necropsy can result in large differences in hepatocellular glycogen thus leading to inaccurate analysis 212 nuclei/mm² 263 nuclei/mm² Mouse Liver

  32. Consistency of Necropsy Conditions Can Affect Morphometric Analysis Variations in exsanguination during necropsy may result in differences in sinusoidal dilatation thus leading to inaccurate analysis Mouse Liver

  33. Consistency of Sample Area Selection for Morphometric Analysis within the Median Lobe of the Mouse Liver 1 2 3 Select samples within approximately the same region of the same lobe of the liver for consistency of analysis. As an assurance of sampling homogeneity, areas should have roughly similar pixel count values.

  34. Quantitation of Periarteriolar Lymphoid Tissue in a Mouse Spleen using Genie and the Aperio Positive Pixel Count Tool H&E Stain Genie Markup Image Aperio Positive Pixel Markup Image Result: Lymphoid tissue comprises 30.1% of positive pixels in splenic cross-sectional area Extrapolating to an entire tissue section demands more robust training than for a simple image.

  35. Quantitation of Splenic Erythropoiesis in a Mouse using Genie™ and Aperio Algorithms Original H&E Stain Genie™EMH Classifier Measuring Erythroid Nuclear Area Using Hematoxylin Channel of Deconvolution Algorithm Counting Erythroid Nuclei Using Nuclear Algorithm

  36. Quantitation of Hepatocellular Necrosis Use of Genie™ as a preprocessing utility to identify regions of hepatic necrosis (red) and areas of normal liver (grey) Subsequent quantitation of necrotic area to allow precise grading

  37. Using the Microvessel Analysis Algorithm to Assess Angiogenesis in a Xenograft Neoplasm Use of the microvessel analysis algorithm to assess angiogenesis in a xenograft neoplasm in a mouse Microvessel analysis provides important information regarding potential antineoplastic effects of pharmaceutical compounds

  38. Using the Microvessel Analysis Algorithm to Count Cells Use of the microvessel analysis algorithm to assess macrophage populations in mouse xenograft neoplasms Threshold algorithm parameters are modified to accommodate the smaller size and shape characteristics of the macrophages

  39. Using the Microvessel Analysis Algorithm to Count Cells Algorithm Output Provides Valuable Cell Population Data • Total Number of Cells • Total Area of Analysis • Total Cell Area • Cell Density/Unit Area • Average Stain Density • Mean Cell Area • Histogram of Cell Areas

  40. Quantitating Dog Thyroid Gland Tissue Components Use of Genie™ as a preprocessing utility to identify thyroid gland follicular epithelium (green), colloid (red) and C-cells (blue) Then quantitate as part of the Genie™ utility to determine area and relative percentage of each separate tissue component.

  41. Measuring Follicular Cell Hypertrophy in Dog Thyroid Gland Use of Genie™ to segregate the thyroid gland follicular epithelium (green) as an intended target tissue for analysis Then apply the nuclear algorithm counting total nuclear numbers in the target tissue. Total Nuclei/Total Target Tissue Area = Mean Follicular Cell Area

  42. Islet Cell Mass of Mouse Pancreas Measurement of Pancreatic Islet Cell Mass using Genie™ Followed by the Colocalization Algorithm (A/B)C=Islet Cell Mass A=Total Islet Area in Section B=Total Pancreas Area in Section C=Pancreatic Weight

  43. Bile Duct Hyperplasia in Rat Liver First pass Genie histology pattern identification with minimal training. Genie™ can simultaneously analyze three or more tissue areas Hyperplastic Bile Ducts = Green Hepatic Parenchyma = Red Periportal Inflammatory Cells = Blue Periductal Collagen = Brown Bile Duct Lumena + Sinusoids = Yellow Then analyze up to three tissue areas using colocalization tool

  44. Cynomolgus Monkey Lung Use of Genie™ as a preprocessing utility to identify regions of bronchiolar epithelium (green) Subsequent isolation and analysis of only bronchiolar epithelium using the positive pixel count or other analysis tool

  45. Summary • The ability to digitize entire slides and perform morphometric analysis on images has been valuable in allowing the rapid and practical measurement of tissue biomarkers for pharmaceutical research and development. • A number of strategies and examples have been presented for using various image analysis algorithms in the measurement of tissue changes and tissue biomarkers. Image analysis of specific target tissues can be particularly challenging in cases with large and morphologically intricate areas of tissue, or when tissue staining is nonspecific. • Genie™, a histology pattern recognition tool, has been introduced as a preprocessing utility capable of identifying and categorizing specific histologic tissue types, thus allowing subsequent analysis of target regions by standard image analysis tools. • Significant challenges remain in developing practical procedures and methods appropriate for the analysis of oncology and toxicology specimens. Recent object recognition advancements may assist in this effort.

  46. Acknowledgements • Ms. Kimberly Merriam, TBG, BMD Novartis Pharmaceuticals • Ms. Jeanette Rheinhardt, TBG, BMD Novartis Pharmaceuticals • Dr. Allen Olson, Aperio Technologies, Inc. • Dr. Kate Lillard-Wetherell, Aperio Technologies, Inc. • Mr. James Deeds, Oncology Research, Novartis Pharmaceuticals • Ms. Veronica Travaglione, Pharmacology, Infinity Pharmaceuticals • Mr. Igor Deyneko, Pharmacology, Infinity Pharmaceuticals • Dr. Humphrey Gardner, TBG, BMD, Novartis Pharmaceuticals • Dr. Steve Potts, Aperio Technologies, Inc • Dr. Reginald Valdez, Novartis Pharmaceuticals • Dr Oliver Turner, Novartis Pharmaceuticals • Mr. Trevor Johnson, Aperio Technologies, Inc. • Others

  47. Frank VoelkerDVM  MS Diplomate ACVPKey bio points / specialties Flagship Biosciences LLC provides biotech, pharmaceutical, and medical device companies with quantitative pathology services. Contact us: www.flagshipbio.com

More Related